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Machine Learning - NAIL029

Title:	Strojové učení
Guaranteed by:	Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2015
Semester:	summer
E-Credits:	3
Hours per week, examination:	summer s.:2/0, Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
Virtual mobility / capacity:	no
State of the course:	taught
Language:	Czech, English
Teaching methods:	full-time

Guarantor:	Mgr. Marta Vomlelová, Ph.D.
Class:	Informatika Mgr. - Teoretická informatika Informatika Mgr. - Matematická lingvistika
Classification:	Informatics > Theoretical Computer Science

Opinion survey results Examination dates SS schedule Noticeboard

Annotation -

Last update: T_KTI (03.05.2012)

The aim of the course is to introduce machine learning as important and in this time very vital field developing in the close connection with artificial intelligence. The course gives a survey of basic branches of machine learning (supervised inductive learning, reinforcement learning, unsupervised learning and knowledge in learning), main problems and methods and some typical algorithms.

Aim of the course -

Last update: Mgr. Marta Vomlelová, Ph.D. (14.05.2021)

The course extends the basic machine learning course.

Course completion requirements -

Last update: Mgr. Marta Vomlelová, Ph.D. (07.06.2019)

The exam consists of a written preparation and an oral part. The requirements are given by the course syllabus.

Literature -

Last update: Mgr. Marta Vomlelová, Ph.D. (20.12.2022)

T. Hastie, R. Tibshirani, J. Friedman: The Elements of Statistical Learning, Springer 2009

G. James, D. Witten, T. Hastie, R. Tibshirani: An Introduction to Statistical learning with Applications in R, Springer, 2014

S.J. Russell, P. Norvig: Artificial Intelligence: A Modern Approach; Prentice Hall, 1995

Requirements to the exam -

Last update: Mgr. Marta Vomlelová, Ph.D. (07.06.2019)

The exam consists of a written preparation and an oral part. The requirements are given by the course syllabus.

Syllabus -

Last update: Mgr. Marta Vomlelová, Ph.D. (07.06.2020)

Linear regression and instance based learning as "extremal points" in the space of models,

the curse of dimensionality, bias-variance tradeoff,

logistic regression, generalized additive models,

model assessment (confidence intervals, crossvalidation, one-leave-out)

decision trees, prunning, missing values, random forest,

rule search PRIM,

model averaging, boosting, random forest,

support vector machines,

Bayesian learning, EM algorithm introduced on an clustering example,

Undirected graphical models, Gaussian processes and Bayesian optimization,

unsupervised learning - market basket analysis, clustering k-means, k-medoids, hierarchical clustering.